Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship
[ 1 ] Instytut Technologii i Inżynierii Chemicznej, Wydział Technologii Chemicznej, Politechnika Poznańska | [ P ] employee
2022
scientific article
english
- artificial intelligence
- chemical structure
- drug design
- machine learning
- neural network
EN The paper presents a comprehensive overview of the use of artificial intelligence (AI) systems in drug design. Neural networks, which are one of the systems employed in AI, are used to identify chemical structures that can have medical relevance. Successful training of neural networks must be preceded by the acquisition of relevant information about chemical compounds, functional groups, and their possible biological activity. In general, a neural network requires a large set of training data, which must contain information about the chemical structure–biological activity relationship. The data can come from experimental measurements, but can also be generated using appropriate quantum models. In many of the studies presented below, authors showed a significant potential of neural networks to produce generalizations based on even relatively narrow training data. Despite the fact that neural network systems have been known for more than 40 years, it is only recently that they have seen rapid development due to the wider availability of computing power. In recent years, there has been a growing interest in deep learning techniques, bringing network modeling to a new level of abstraction. Deep learning allows combining what seems to be causally distant phenomena and effects, and to associate facts in a way resembling the human mind.
06.08.2021
e1568-1 - e1568-18
Article number: e1568
CC BY (attribution alone)
czasopismo hybrydowe
final published version
06.08.2021
in press
public
200
11,4